Knowledge catalogue
arXiv:2610.18025v1 Announce Type: cross Abstract: Test-time scaling has enabled LLMs to solve increasingly complex reasoning, discovery, and optimization tasks. However, existing approaches often gath
arXiv:2507.23449v3 Announce Type: replace Abstract: One-class learning is an effective methodology for time series anomaly detection, yet kernel one-class approaches often compare sequences in a point
arXiv:2610.16106v1 Announce Type: new Abstract: Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerab
arXiv:2610.14196v1 Announce Type: cross Abstract: Late-interaction (multi-vector) models represent documents using a set of token-level vectors, providing a middle ground between cross-encoders and de
arXiv:2512.10581v2 Announce Type: replace Abstract: Current methods for all-in-one image restoration increasingly rely on complex mechanisms, such as Mixture-of-Experts or prompt learning, to handle d
arXiv:2610.18472v1 Announce Type: new Abstract: Few-step distillation reduces the sampling cost of diffusion and flow models, but existing approaches often require auxiliary score models or specialize
arXiv:2610.17620v1 Announce Type: new Abstract: Routine inspection of power distribution infrastructure remains challenging for utilities operating geographically dispersed networks, where conventiona
arXiv:2610.15204v1 Announce Type: cross Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited:
arXiv:2610.17226v1 Announce Type: cross Abstract: Most machine-learning interatomic potentials (MLIPs) are trained on gas-phase data, which limits their use for chemistry in solution. Here, we introdu
arXiv:2610.17808v1 Announce Type: new Abstract: Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fa
arXiv:2610.16680v1 Announce Type: cross Abstract: Resampling is the standard remedy against weight degeneracy in particle filters, yet its effect on the spectrum of the particle second-moment matrix i
arXiv:2610.14405v1 Announce Type: cross Abstract: Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. Howev
arXiv:2610.17323v1 Announce Type: new Abstract: Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protec
arXiv:2606.19774v2 Announce Type: replace Abstract: Action chunking enables robot policies to produce temporally coherent behavior, but generating multi-step action sequences with flow-based policies
arXiv:2610.15560v1 Announce Type: new Abstract: Existing navigation systems commonly rely on dedicated depth sensors or explicit depth reconstruction, while learning to generate trajectories directly
As strata is somewhat new, I assume that some people din't yet learn how to finetune it for best performance on their machine. I find documentation a bit hard to navigate (use your agents for this!),
arXiv:2610.15095v1 Announce Type: new Abstract: LLM multi-agent systems split work across agents that communicate through text. An agent summarizes its findings, another agent receives the text. This
arXiv:2610.17540v1 Announce Type: cross Abstract: Textual reasoning before acting has improved vision-language-action (VLA) models, but at the cost of latency: every action conditioned on the reasonin
arXiv:2610.14440v1 Announce Type: new Abstract: Asynchronous reinforcement-learning post-training of large language models is computationally efficient but inherently off-policy: the learner updates o
arXiv:2610.14513v1 Announce Type: new Abstract: Robotic platforms combine multi-modal sensors to operate across diverse conditions, but most dense correspondence methods are limited to RGB-RGB or a sm
arXiv:2610.18256v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a popular training paradigm for LLMs, providing dense, token-level supervision by aligning a model's o
arXiv:2610.15659v1 Announce Type: new Abstract: Centrality measures rank nodes even when the observed differences can be explained by ordinary structural variation. We therefore study a preceding ques
arXiv:2610.15058v1 Announce Type: new Abstract: Larger sample budgets benefit from higher sampling temperatures in LLMs, but what temperature tuning optimizes and why it works remain poorly understood
arXiv:2610.16977v1 Announce Type: new Abstract: Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends c
arXiv:2610.16165v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning
arXiv:2610.14265v1 Announce Type: new Abstract: Recent advances in multi-modal large language models have shifted toward the 'thinking with images' paradigm to improve reasoning faithfulness, yet exis
arXiv:2606.20310v2 Announce Type: replace Abstract: Video reward models are central to aligning video diffusion models with human preferences, yet existing ones are external vision-language models tha
arXiv:2610.17065v1 Announce Type: new Abstract: Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical
arXiv:2610.16921v1 Announce Type: cross Abstract: Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misalign
arXiv:2610.15721v1 Announce Type: new Abstract: Cross-model KV cache sharing reduces prefill computation by allowing a receiver model to reuse prefix KV states generated by a related sender model. Exi
arXiv:2603.18091v2 Announce Type: replace Abstract: Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candi
arXiv:2603.25063v2 Announce Type: replace-cross Abstract: Recent agentic systems demonstrate that large language models can generate complex codebases from natural language prompts. Despite this progr
arXiv:2610.15807v1 Announce Type: new Abstract: Generating coherent videos grounded in a unified underlying world remains a major challenge, particularly under large viewpoint changes, where existing
arXiv:2602.24111v2 Announce Type: replace Abstract: Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagno
arXiv:2601.02759v2 Announce Type: replace Abstract: Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring target-domain retraining, fine-tun
arXiv:2610.17657v1 Announce Type: new Abstract: An embodied brain, or System 2, must turn an abstract goal into actions while discovering relevant scene conditions and checking whether executed skills
arXiv:2610.17531v1 Announce Type: new Abstract: While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact
arXiv:2610.14846v1 Announce Type: new Abstract: Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, wh
arXiv:2610.15365v1 Announce Type: cross Abstract: Large language models (LLMs) can produce harmful content while generating a response. A useful safeguard should not only detect this risk during gener
arXiv:2610.17726v1 Announce Type: new Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new sk
arXiv:2603.18572v2 Announce Type: replace-cross Abstract: Deep unrolled models (DUM) have become the state-of-the-art for accelerated MRI reconstruction, yet their robustness under domain shift remain
arXiv:2610.14696v1 Announce Type: new Abstract: Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian
arXiv:2601.17673v2 Announce Type: replace Abstract: Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object loc
arXiv:2610.12941v2 Announce Type: replace Abstract: Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurrin
arXiv:2503.07035v3 Announce Type: replace Abstract: Existing incremental learning (IL) methods rely on strong assumptions: incoming tasks either introduce exclusively new classes or new domains (i.e.,
arXiv:2511.13714v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity rem
arXiv:2204.09334v4 Announce Type: replace-cross Abstract: Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle
[UPDATE / RETRACTION: The '100% GTO' claim had a prompt-leakage bug] We patched the repo to pass strictly neutral states, here are the real, honest numbers: • Latency: Still ~2ms (ONNX + Absorbing Mar
arXiv:2610.17252v1 Announce Type: cross Abstract: Search-based policy learning, such as Monte Carlo tree search (MCTS), offers the flexibility to trade runtime computation for data efficiency and adap
arXiv:2610.17383v1 Announce Type: new Abstract: Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing
arXiv:2610.16357v1 Announce Type: new Abstract: Agentic video reasoning systems gather and assess video evidence across stages, selecting relevant spans as visual evidence for answering questions. New
arXiv:2610.17902v1 Announce Type: new Abstract: Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Al
arXiv:2610.18858v1 Announce Type: new Abstract: Multimodal large language models have advanced rapidly, but most remain optimized for single-image inputs. Multi-image reasoning is costly because each
arXiv:2610.14517v1 Announce Type: new Abstract: During a network forward call, diffusion backbones naturally produce many intermediate activations. If we evaluate each activation against an identical
arXiv:2603.27158v3 Announce Type: replace Abstract: While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep lear
arXiv:2610.14007v1 Announce Type: new Abstract: A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them clos
arXiv:2610.17844v1 Announce Type: new Abstract: Spatial understanding is a necessary step in moving multimodal large language models (MLLMs) toward reasoning about the 3D physical world. Their visual
arXiv:2610.15035v1 Announce Type: new Abstract: Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating misma
arXiv:2610.17242v1 Announce Type: new Abstract: Can a vision-language model tell left from right? Modern VLMs solve visual puzzles and follow hour-long videos, yet when asked whether the camera moved
arXiv:2610.15929v1 Announce Type: new Abstract: Recent trackers built on foundation models such as the Segment Anything Model (SAM) achieve strong performance by propagating object masks through memor